The Role of Outcomes Research in Defining and Measuring Value in Benefit Decision

OBJECTIVE
To identify ways that health care leaders at all levels can quantify the value proposition, thus influencing health care delivery and improving patient care.


SUMMARY
Payers and providers need to support, with rigorous research, the value proposition for customers. Outcomes research focusing on clinical and cost-effectiveness analysis can provide an understanding of successful, replicable interventions. Randomized controlled trials and observational studies can be used to reinforce and refine the business proposition in health care, and they can be integrated to target populations needing health care services. Evaluations using clinical and outcomes research can also predict what is likely to be successful in the future. To maximize the business value of projects, they must incorporate a prospective evaluation component that includes asking the right research questions, identifying an appropriate time period, including a targeted population, articulating a replicable intervention, and determining the correct statistical analysis.


CONCLUSION
Well-designed studies to analyze specific patient populations and their patterns of care can be used to determine a generalizable model to refine successful interventions that meet the critical value proposition for employers.

T he ability to demonstrate value is a tremendous concern in health care. This creates opportunities for analytic applications that provide employers and policymakers with meaningful information. Since employers continue to fund a considerable proportion of health care insurance coverage in the United States, these applications have to demonstrate the business proposition. Payers and providers need to quantify the value proposition-a summary of the customer segment, competitor targets, and the core differentiation of one' s product from the offerings of competitors-for customers through rigorous research. Fortunately, in the recent past, health services research improvements have focused on health care' s growing need for sophisticated information. Clinical and outcomes research has been used to enhance our understanding of successful interventions, methods for increasing quality, and evaluation of the cost-effectiveness of chronic care interventions. Most important, these clinical and outcomes evaluations have assisted in defining population-based targets. By summarizing and disseminating these results, employers may be convinced that health investments will improve the corporate bottom line.
A critical component of future studies is the need to ask the right research questions. All too often, investigators fail to ask the questions that are most meaningful for health care payers, including employers. For example, employers may be interested in clinical outcomes as an intermediate indicator of the value of their investment, but they would be most interested in determining whether health care interventions create cost advantages. The nature of the cost advantages must include direct costs, such as reduced hospitalizations, physician visits, and medication use. But employers also expect that these studies will extend to the key elements that affect their enterprise: presenteeism, absenteeism, and contributions to their profit statement. This requires a new orientation in outcomes research and the need to develop measures, techniques, and methods that translate clinical and cost parameters to company profitability. The only way that this effort can be fruitful is through multidisciplinary efforts to extend health outcomes research methods.

I II I R Re es se ea ar rc ch h A Ap pp pr ro oa ac ch he es s
The 2 principal research approaches are randomized controlled trials (RCTs), which are designed to show causation, and observational studies (OBSs), which reflect more of a real-world setting. A third approach is cost-effectiveness analyses (CEAs), which incorporate data from the 2 prior study types to develop an understanding of health care value. Each type of study has a place in expanding our understanding of health care interventions, and these studies can be integrated to increase impact. OBSs and CEAs are being used much more frequently to inform the design and interpretation of results from randomized trials. Two familiar examples are the Women' s Health Initiative and the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) studies. Figure 1 demonstrates how we can incorporate RCTs and OBSs within the context of available evidence to focus our understanding of health care value and to refine future study designs. It is important to note that the integration of these study types is dynamic: as new studies become available, key components of the cost-effectiveness model may be revised and integrated. This then yields new estimates of the benefit generated by a health care intervention (e.g., medication or care management). The results of the CEA can then be evaluated using traditional study designs. Currently, most health care interventions designed to improve health care outcomes and value propositions fail to incorporate available information to target the population of interest and to tailor the intervention appropriately. Figure 1 illustrates that it would be helpful to build cost-effectiveness models to determine key questions and assumptions that may drive our understanding of the value of specific interventions. Subsequently, health care leaders might consider the value of conducting randomized trials within the context of their employee populations to test these questions. The implementation of the Part D benefit expansion in Medicare, for example, might reasonably include a test of the value proposition for drug coverage in the targeted population.
RCTs give strong internal validity but are not necessarily indicative of the real world. Many commentators have remarked about the difficulty of designing real-world RCTs to address key health outcomes questions. OBSs, on the other hand, suffer from their own set of validity threats. For example, these studies generally do not include methods for controlling patient selection.
To compensate for this, physicians or care managers may target interventions to specific populations or may use the intervention more frequently in certain clinical situations. This targeting compromises our ability to conclude that the intervention, rather than the population, was the cause of any positive outcomes. Further, neither the provider nor the patient is blind to the intervention; consequently, either or both may be positively (or negatively) predisposed to the intervention, and this may affect the results. In short, OBSs suffer from the fact that alternative explanations for the outcomes can be abundant, and lack of uniformity in targeted populations, protocols, and providers remains problematic. But OBSs generally have large study populations and allow researchers to observe behavior outside the rigid constraints of a randomized trial protocol. They also allow comparisons among multiple treatments simultaneously, tend to be less costly than RCTs, and can be conducted over a period tailored to the study' s objectives.
Although opinions vary about the utility and validity of RCTs and OBSs, academics and formulary decision makers consider results generated from RCTs most valid and results of OBSs as less valid because they may overestimate treatment effects. However, an interesting series of articles in the New England Journal of Medicine in 2000 illustrates how RCTs and OBSs frequently produce similar results. One of these articles analyzed articles published in 5 major medical journals from 1991 to 1995. They identified meta-analyses of RCTs and meta-analyses of either cohort or case-control studies that assessed the same intervention. The mean results of the OBSs were remarkably similar to those of the RCTs. Topics assessed included effectiveness of bacille Calmette-Guerin vaccine in preventing active tuberculosis, mammography and mortality from breast cancer, cholesterol levels and death due to trauma and all-causes, treatment of hypertension and stroke, and treatment of hypertension and coronary heart disease. They concluded that the results of well-designed OBSs (with either a cohort or a case-control design) do not systematically overestimate the magnitude of the effects of treatment as compared with the results of RCTs on the same topic. 1 This indicates that, in some cases, clinical trials and OBSs may produce similar results, thus providing better evidence regarding internal and external study validity. By suggesting the dimension of a treatment effect that researchers can expect, RCTs can at least help researchers determine the sample sizes needed to test a question in an observational cohort.
Observational data can also be used to identify patterns of care and appropriate intervention targets and population cohorts. From these data, it may be possible to tailor incentives and interventions to obtain positive outcomes. Consider the study that sought to measure patients' persistency in taking their antihypertensive medications. The study population comprised patients who had recently initiated therapy. The purpose of this study was to determine whether and when newly started patients discontinued medication therapy. The measures of persistence

The Role of Outcomes Research in Defining and Measuring Value in Benefit Decisions
included time to discontinuation, switch, or augmentation; medication possession ratio (the percentage of time in the study that a medication was in the patient' s possession); and proportion of days covered with medication (PDC; the percentage of patients in a treatment cohort who had a medication in their possession at any given time). Figure 2 plots the findings on PDC. 2 The PDC analysis provides insight into medication-taking behavior by determining whether each patient has medication available (through refills) each day in the year following the initiation of therapy. Figure 2 shows that a significant percentage of the cohort drops therapy in the first 31 days. In other words, more than 40% of patients discontinued antihypertensive therapy in the first month. The graph also reflects that some people refill their prescriptions late, and adherence drops again at the end of 60 days. At approximately 90 days, persistence appears to stabilize, as those individuals who have continued therapy at 90 days appear to continue their antihypertensive medication use for the remainder of the 1-year period. Those started on angiotensin-converting enzyme inhibitors have a better persistency compared with those who might have started on diuretics. 2 This leads to several conclusions: • Particular classes of drugs may have greater persistence benefit than others. • Patients who are persistent 90 days after initiating therapy are likely to continue using the medications for at least a year. It' s not clear that it is cost effective to target these individuals for adherence interventions. • Patients who fill only one 30-day prescription may be expensive targets for adherence intervention programs since they may need complicated efforts to improve their responsiveness to treatment. • Intermittent users (those who fill their initial 3 medications with some delays) may be appropriate targets for adherence interventions. It remains to be seen whether these interventions are cost effective. Most importantly, this approach does not simply identify a behavior problem. It also stimulates consideration of the interventions needed to change patient behavior and the costs associated with those interventions.
Many OBSs, as with the data in Figure 2, address limited periods of analysis, and lengthening the observation period can reveal some interesting and important findings. An OBS conducted with collaborators at the University of Southern California, the University of California at Los Angeles, and Cedars Sinai Hospital in Los Angeles examined the effects of ethnicity on a systemic lupus erythematosus population' s health care utilization and direct medical costs. This study addressed a controversy in the field: people of color and whites react very differently to this disease, and some preliminary studies have implied that African Americans tend to become sicker more quickly and to incur additional health care treatment costs. To the contrary, the results showed that the total cost of care for whites and African Americans were increasingly similar during the 8 years of the study. 3 Hispanic patients tended to have shorter eligibility periods compared with other cohorts (approximately 50% versus 70% were eligible at month 36, respectively). Over time, the cost of care for Hispanics, including inpatient use, prescription costs, and outpatient/ physician services costs, was dramatically lower than that for other cohorts. Since these are observational data, it' s not possible to determine the cause of these differences, but it is clear that the differences between whites and African Americans are smaller than the differences between Hispanics and either whites or African Americans.
Thus, RCTs are powerful research designs that demonstrate cause and effect within limited populations, and OBSs provide insight into the real-world use of health services, costs, and interventions. Each study has its own set of limitations, but both can be used to provide meaningful outcomes for employed populations.

I II I C Co os st t--E Ef ff fe ec ct ti iv ve en ne es ss s A An na al ly ys si is s
The CEA (which considers cost minimization/cost consequence, cost effectiveness, cost utility, and cost benefit) has become a useful tool in comparing particular types of medication products or classes. However, these analytic approaches can provide important insight into areas of general health policy questions that can be used by employers. A recent CEA we conducted on the transition of second-generation antihistamines (SGAs) from prescription to over-the-counter (OTC) status is an example. First-generation antihistamines (FGAs), many of which have been available OTC for years, have been associated with increased risk of unintentional injuries, fatalities, and reduced productivity. Although manufacturers of SGAs expressed concern regarding the use of these products over the counter, the U.S.

The Role of Outcomes Research in Defining and Measuring Value in Benefit Decisions
Administration considered an evaluation of their prescription status in 2003. Using a societal impact perspective (amelioration of allergic rhinitis symptoms and avoidance of motor vehicle, occupational, public, and home injuries and fatalities), a study of the cost-effectiveness of moving SGAs to OTC status was conducted. 4 We used a simulation model, comparing the transition to OTC status with retaining prescription-only status for a hypothetical cohort of individuals with allergic rhinitis. Costs and effectiveness estimates were obtained from the medical literature and national surveys. The study found that OTC SGA availability was associated with mean annual savings of $4 billion (range: $2.4 billion to $5.3 billion) or $100 (range: $64 to $137) per allergic rhinitis sufferer and 135,061 time-discounted qualityadjusted life-years (range: 84,913 to 191,802). Even when the study assumptions were varied dramatically, cost savings were realized. The impact to society was positive, mainly because of reduced adverse outcomes related to FGA-induced sedation.
As noted in Figure 1, CEA and cohort studies can also be used to identify the programs that work and the populations that can best be targeted. Based on previous research, it is reasonable to expect that today' s high-cost patients will be tomorrow' s high-cost users, but some high-cost users regress to the mean. Of critical importance is the determination of the interventions that might successfully alter high-cost users' behavior so that they become low-cost users. Population-clustering approaches show some promise in the identification of specific groups of patients; combined with the evaluation of intervention effectiveness, these population-clustering approaches may help us develop targeted approaches with some likelihood of success. Figure 3 provides an example of clustering analysis in a Medicaid diabetes population. This study focused on a sample of Medicaid members in southern California over an 18-month period. The purpose of this study was to determine the extent to which patients maintained stable patterns of use between the first 9-month period and the second 9-month period available for analysis. It was expected that deriving transition clusters may illuminate groups with shared characteristics that are precursors to more expensive health care patterns. Total medication costs, patient age, and comorbidities (using the Chronic Disease Score [CDS]) were measured. Using the data in the 2 periods, 5 clusters were revealed. This graphical depiction of the clusters shows that 2 populations were very similar, 2 had some similar characteristics, and 1 cohort was distinctly different from the others. Individuals among the population clusters experienced very different cost transitions, but those within the clusters showed similar cost profiles. These patient characteristics could potentially be used to develop targeted interventions and to test the extent to which they resulted in clinical and cost improvements. It would be particularly interesting to test the use of clustered populations in directing some of the interventions that have been addressed in other parts of this supplement: • Adjusting patient cost through coupons, vouchers, or copayments • Encouraging adherence • Targeting members in greatest need (e.g., the elderly, users of multiple medications) • Promoting best practices (e.g., safety, reduction in multiple medications) • Maximizing patient-reported outcomes (e.g., quality of life, preferences) The creation of Part D medication coverage introduced a massive shift in insurance that will affect health care for the foreseeable future. The structure of the benefit for the dually eligible population, for example, may create significant barriers to continuity of care. This population is allowed to change coverage every month, and the participation requirement results in short-term coverage variability that will be difficult to change without legislation. From the perspective of the researcher, the law may also compromise data availability for long-term research. As the lupus   study demonstrated, the availability of longitudinal data may affect the nature of our conclusions regarding patterns of care. Even if such data are available, it may be difficult to distinguish effects of the policy change from the effects of benefit design or the effects of patient behavior. Issues associated with consumer-directed health plans will change the market dynamic dramatically, and it is important to maintain an active monitoring process to determine whether the consequences of change are as intended. The legislative provisions for health savings account (HSA) expansion are already creating movement away from employer coverage. To some degree, employers simply seek a consistent and manageable health benefit cost structure, and HSAs help them meet this goal. However, employers will also need to understand the role that benefit design plays in employee health care behavior. For example, if increased out-of-pocket costs negatively affect medication adherence, how will employee productivity be affected? Identifying the best methods for communicating health information to employees will be key to success, and translating health care investment dollars into return on investment for employers will be the insurer' s responsibility.

I II I C Co on nc cl lu us si io on n
Many leaders in the research field are beginning to consider the application of their work to health care value. In the last 3 to 4 years, the National Institutes of Health (NIH) has augmented its traditional basic research with applied research in a bench-to-bedside focus. NIH has mandated that at least 30% of its budget be directed toward research projects that will be translated to improving patient care. There is also increasing recognition that the real world is considerably different from the artificial environment of a clinical trial. Patient behavior and the presence of multiple comorbidities may affect responsiveness to care and may demand greater attention to care management. Clearly, our approach to the value proposition must be multidisciplinary and should include populations that are representative of disease, ethnicity, and population diversity. Employers, insurers, and researchers all have roles in directing the health care system toward positive change. The complexity of the health care environment increases the likelihood that unintended consequences may result from even simple and seemingly transparent changes. In the absence of studies to quantify the additional value of therapeutic interventions, therapies-drugs and other health care interventions-will essentially evolve into isolated commodities. By focusing only on the purchase of health care interventions, the critical effect of this care on personal health and the value of improved health to society goes unrecognized.

DISCLOSURES
This article is based on the proceedings of a symposium held on April 5, 2006, at the Academy of Managed Care Pharmacy' s 18th Annual Meeting and Showcase in Seattle, Washington, which was supported by an educational grant from sanofi-aventis and sponsored by the Benefit Design Institute. The author received an honorarium from sanofi-aventis for participation in the symposium. He reports no affiliations with or financial interest in any commercial organization that poses a conflict of interest with the presentation on which this article is based.